1. Field of the Invention
This invention relates generally to communication systems, and, more particularly, to wireless communication systems.
2. Description of the Related Art
Base stations in wireless communication systems provide wireless connectivity to users within a geographic area, or cell, associated with the base station. In some cases, the cell may be divided into sectors that subtend a selected opening angle (e.g., three 120° sectors or six 60° sectors) and are served by different antennas. The wireless communication links between the base station and each of the users typically includes one or more downlink (DL) (or forward) channels for transmitting information from the base station to the mobile unit and one or more uplink (UL) (or reverse) channels for transmitting information from the mobile unit to the base station. The uplink and/or downlink channels include traffic channels, signaling channels, broadcast channels, paging channels, pilot channels, and the like. The channels can be defined according to various protocols including time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), orthogonal frequency division multiple access (OFDMA), as well as combinations of these techniques. The geographical extent of each cell may be time variable and may be determined by the transmission powers used by the base stations, access point, and/or mobile units, as well as by environmental conditions, physical obstructions, and the like.
Mobile units are assigned to base stations based upon properties of the channels of supported by the corresponding air interface. For example, in a traditional cellular system, each mobile unit is assigned to a cell on the basis of criteria such as the uplink and/or downlink signal strength. The mobile unit then communicates with that serving cell over the appropriate uplink and/or downlink channels. Signals transmitted between the mobile unit and the serving cell may interfere with communications between other mobile units and/or other cells. For example, mobile units and/or base stations create intercell interference for all other sites that use the same time/frequency resources. The increasing demand for wireless communication resources has pushed service providers towards implementing universal resource reuse, which increases the likelihood of intercell interference. In fact, the performance of modern systems is primarily limited by intercell interference, which dominates the underlying thermal noise.
Intercell interference can be reduced in several ways, for example through frequency planning, soft handoff, or beamforming multiple antennas. For example, multiple-input-multiple-output (MIMO) techniques may be employed when the base station and, optionally, the user terminals include multiple antennas. For example, a base station that includes multiple antennas can concurrently transmit multiple independent and distinct signals on the same frequency band to same user or multiple users in a cell/sector. MIMO techniques are capable of increasing the spectral efficiency of the wireless communication system roughly in proportion to the number of antennas available at the base station.
Although conventional wireless communication systems attempt to reduce the effects of intercell interference using various interference cancellation techniques, alternative approaches recognize that intercell “interference” is actually caused by signals that include valuable information. For example, on the uplink, intercell interference at one cell site is merely a superposition of signals that were intended for other cell sites, i.e., the intercell interference is formed of mobile unit signals that have been collected at the wrong place. If these signals could be properly classified and routed, they would cease to be interference and would become useful in the detection of the information they bear. While challenging, combining information received at disparate sites is theoretically possible because the cell sites are connected to a common and powerful backbone network. This is tantamount to recognizing that a network of wireless cell sites can form a large distributed multi-access channel and all users can be served through all the cell sites. This ambitious approach leverages the bandwidth available in wireline networks to transcend intercell interference and alleviate the wireless bottleneck. For example, a new class of multi-antenna techniques called Inter-Base Station MIMO (IBS-MIMO) has been proposed to enhance air-interface performance by enabling concurrent transmission of superposed signal waveforms from antennas at different base stations to one or more mobile terminals in such a way that the resulting mutual interference is suppressed.
On the downlink, IBS-MIMO techniques coordinate different base stations so that they concurrently transmit (in a coordinated fashion) superposed signal waveforms from their antennas to one or more mobile units in such a way that the resulting mutual interference is suppressed and the signals from multiple base stations may be coherently combined at each mobile unit. In this process, the signal destined for a specific mobile unit can be transmitted from different base stations. The radio access network provides control signaling and/or data plane exchanges to coordinate the base stations so that their transmissions can be coherently combined. For example, each user's signal can be transmitted simultaneously from multiple base station antennas (possibly spatially distributed). The signals are weighted and pre-processed so that intercell interference is mitigated or completely eliminated by coherent combination of the superposed signals from the different base stations. Under the assumption of full coordination between the M antennas of all B base stations in the system, the behaviour of the system is the same as a MU-MIMO (multiuser multi-input multi-output) system with B*M distributed antennas. The system typically employs hybrid per-cluster power constraints so that clusters of antennas in the same site are subject to a sum-power constraint and power is not shared between antennas belonging to different clusters.
Numerous constraints, including constraints on the available backhaul bandwidth, may make it difficult or impossible to implement full network coordination in real systems. For example, implementing full network coordination may increase the backhaul overhead required for signaling and data transmissions by orders of magnitude relative to the backhaul bandwidth used for conventional uncoordinated transmission. For example, assuming full coordination between B=10 base stations and a star network topology, the amount of data traffic in the backhaul increases by approximately a factor of 10. Even if this additional backhaul bandwidth can be accommodated within the system, system designers may consider this an unacceptably large cost to achieve the performance gains provided by full coordination of the base stations in the network.
The backhaul bandwidth and/or overhead can be reduced by limiting coordination to a subset of the base stations and/or cells within the system. Different approaches have been considered in order to limit the coordination to only a subset of the cells in the system. One approach to coordinating uplink and downlink transmissions divides users into different groups that use orthogonal resources such as orthogonal codes, time intervals, frequencies, and the like. Joint detection can be used only for users belonging to the same group. In this technique, weak users (i.e. users at the edge of the cells) are grouped together and the base-station coordination is realized starting from the weak users until a predetermined constraint on the backhaul is achieved. Users are assigned to the groups using channel state information that is averaged over a relatively long time period, such as several seconds. Consequently, this technique does not consider the dynamics of the uplink and/or downlink channels.
One alternative base station selection algorithm selects base stations to minimize the power used to satisfy an equal-rate requirement for uplink transmission. Power allocation, receive (linear) beamforming and cluster assignment are jointly realized for the selected base stations. The equal-rate requirement a test you provide a minimum data transmission rate or quality-of-service level for each user. This requirement is typically used for circuit-based transmissions such as voice service. However, this base station selection algorithm does not incorporate information that reflects the dynamic, time-variable, channel conditions. For example, the algorithm does not schedule or allocate resources based upon the changing channel conditions. Consequently, the main limitation of this work is the lack of diversity with respect to changing channel conditions.
Another alternative dynamic clustering technique selects base stations to maximize the sum-rate for uplink transmissions. In this technique, each base station implements a separate scheduler to choose/schedule users and then a central entity forms clusters of base stations associated with the scheduled users. For example, for each time slot one user per cell is selected using round robin scheduling. At that time slot and for those specific users selected, the algorithm chooses the best base stations in order to serve those users using joint combining.